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数据污染冲击安全防线,国安部:警惕人工智能“数据投毒”
转自:北京日报客户端 国家安全部今天(5日)发布安全提示文章,人工智能的训练数据存在良莠不齐的问题,其中不乏虚假 信息、虚构内容和偏见性观点,造成数据源污染,给人工智能安全带来新的挑战。 促进AI模型的应用。数据资源的日益丰富,加速了"人工智能+"行动的落地,有力促进了人工智能与经 济社会各领域的深度融合。这不仅培育和发展了新质生产力,更推动我国科技跨越式发展、产业优化升 级、生产力整体跃升。 数据污染冲击安全防线 高质量的数据能够显著提升模型的准确性和可靠性,但数据一旦受到污染,可能导致模型决策失误甚至 AI系统失效,存在一定的安全隐患。 投放有害内容。通过篡改、虚构和重复等"数据投毒"行为产生的污染数据,将干扰模型在训练阶段的参 数调整,削弱模型性能、降低其准确性,甚至诱发有害输出。研究显示: 造成递归污染。受到数据污染的人工智能生成的虚假内容,可能成为后续模型训练的数据源,形成具有 延续性的"污染遗留效应"。当前,互联网AI生成内容在数量上已远超人类生产的真实内容,大量低质量 及非客观数据充斥其中,导致AI训练数据集中的错误信息逐代累积,最终扭曲模型本身的认知能力。 引发现实风险。数据污染还可能引发一系 ...
深度|95后Scale AI创始人:AI能力指数级增长,生物进化需要百万年,脑机接口是保持人类智慧与AI共同增长的唯一途径
Z Potentials· 2025-07-28 04:17
Core Insights - The article discusses the rapid advancement of AI technology and its implications for human evolution and society, emphasizing the need for brain-computer interfaces to keep pace with AI development [5][7][22]. Group 1: AI and Data - AI is compared to oil, serving as a crucial resource for future economies and military capabilities, with the potential for unlimited growth through self-reinforcing cycles [22][23]. - Data is highlighted as the new "oil," essential for feeding algorithms and enhancing AI capabilities, with companies competing for data center dominance [23][24]. - The three key components for AI development are algorithms, computational power, and data, with a focus on improving these elements to enhance AI performance [24][25]. Group 2: Brain-Computer Interfaces - Brain-computer interfaces (BCIs) are seen as the only way to maintain human relevance alongside rapidly advancing AI, despite the significant risks they pose [7][22]. - Potential risks of BCIs include memory theft, thought manipulation, and the possibility of creating a reality where individuals can be controlled or influenced by external entities [6][7][26]. - The technology could enable profound enhancements in human cognition, allowing individuals to access vast amounts of information and think at superhuman speeds [9][10]. Group 3: Scale AI - Scale AI, founded by Alexandr Wang, provides essential data support for major AI models, including ChatGPT, and is valued at over $25 billion [2][10]. - The company initially gained recognition for creating large-scale datasets and has since expanded its focus to include partnerships with significant clients, including the U.S. Department of Defense [11][56]. - Scale AI's growth trajectory has been rapid, expanding from a small team to approximately 1,100 employees within five years, with a strong emphasis on the autonomous driving sector [64].
3D高斯泼溅算法大漏洞:数据投毒让GPU显存暴涨70GB,甚至服务器宕机
量子位· 2025-04-22 05:06
梦晨 发自 凹非寺 量子位 | 公众号 QbitAI 随着3D Gaussian Splatting(3DGS)成为新一代高效三维建模技术,它的自适应特性却悄然埋下了安全隐患。在本篇 ICLR 2025 Spotlight 论文中,研究者们提出首个专门针对3DGS的攻击方法——Poison-Splat,通过对输入图像加入扰动,即可显著拖慢训练速度、暴 涨显存占用,甚至导致系统宕机。这一攻击不仅隐蔽、可迁移,还在现实平台中具备可行性,揭示了当前主流3D重建系统中一个未被重视的 安全盲区。 引言:3D视觉的新时代与未设防的后门隐患 过去两年,3D视觉技术经历了飞跃式发展,尤其是由 Kerbi等人在2023年提出的 3D Gaussian Splatting (3DGS) ,以其超高的渲染效率 和拟真度,一跃成为 替代NeRF的3D视觉主力军 。 你是否用过 LumaAI、Spline 或者 Polycam 之类的应用上传图片生成三维模型?它们背后很多就用到了3DGS技术。3D高斯泼溅无需繁重 的神经网络,仅靠一团团显式的、不固定数量的3D高斯点即可构建逼真的三维世界。 但你知道吗?这个看起来高效又灵活的"新王者" ...